Implementation and optimization of the cascade classifier algorithm for UAV detection and tracking

Neural networks (NNs) are now being extensively utilized in various artificial intelligence platforms specifically in the area of image classification and real-time object tracking. We propose a novel design to address the problem of real-time unmanned aerial vehicle (UAV) monitoring and detection using a Zynq UltraScale FPGA-based convolutional neural network (CNN). The biggest challenge while implementing real-time algorithms on FPGAs is the limited DSP hardware resources available on FPGA platforms. Our proposed design overcomes the challenge of autonomous real-time UAV detection and tracking using a Xilinx’s Zynq UltraScale XCZU9EG system on a chip (SoC) platform. Our proposed design explores and provides a solution for overcoming the challenge of limited floating-point resources while maintaining real-time performance. The solution consists of two modules: UAV tracking module and neural network–based UAV detection module. The tracking module uses our novel background-differencing algorithm, while the UAV detection is based on a modified CNN algorithm, designed to give the maximum field-programmable gate array (FPGA) performance. These two modules are designed to complement each other and enabled simultaneously to provide an enhanced real-time UAV detection for any given video input. The proposed system has been tested on real-life flying UAVs, achieving an accuracy of 82%, running at the full frame rate of the input camera for both tracking and neural network (NN) detection, achieving similar performance than an equivalent deep learning processor unit (DPU) with UltraScale FPGA-based HD video and tracking implementation but with lower resource utilization as shown by our results.

Recent advancements in visual object tracking have markedly improved the capabilities of unmanned aerial vehicle (UAV) tracking, which is a critical component in real-world robotics applications. While the integration of hierarchical lightweight networks has become a prevalent strategy for enhancing efficiency in UAV tracking, it often results in a significant drop in network capacity, which further exacerbates challenges in UAV scenarios, such as frequent occlusions and extreme changes in viewing angles. To address these issues, we introduce a novel family of UAV trackers, termed CGTrack, which combines explicit and implicit techniques to expand network capacity within a coarse-to-fine framework. Specifically, we first introduce a Hierarchical Feature Cascade (HFC) module that leverages the spirit of feature reuse to increase network capacity by integrating the deep semantic cues with the rich spatial information, incurring minimal computational costs while enhancing feature representation. Based on this, we design a novel Lightweight Gated Center Head (LGCH) that utilizes gating mechanisms to decouple target-oriented coordinates from previously expanded features, which contain dense local discriminative information. Extensive experiments on three challenging UAV tracking benchmarks demonstrate that CGTrack achieves state-of-the-art performance while running fast. Code will be available at https://github.com/Nightwatch-Fox11/CGTrack.

Unmanned aerial vehicle (UAV) tracking is of great significance for a wide range of applications, such as delivery and agriculture. Previous benchmarks in this area mainly focused on small-scale tracking problems while ignoring the amounts of data, types of data modalities, diversities of target categories and scenarios, and evaluation protocols involved, greatly hiding the massive power of deep UAV tracking. In this article, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers. WebUAV-3M contains over 3.3 million frames across 4,500 videos and offers 223 highly diverse target categories. Each video is densely annotated with bounding boxes by an efficient and scalable semi-automatic target annotation (SATA) pipeline. Importantly, to take advantage of the complementary superiority of language and audio, we enrich WebUAV-3M by innovatively providing both natural language specifications and audio descriptions. We believe that such additions will greatly boost future research in terms of exploring language features and audio cues for multi-modal UAV tracking. In addition, a fine-grained UAV tracking-under-scenario constraint (UTUSC) evaluation protocol and seven challenging scenario subtest sets are constructed to enable the community to develop, adapt and evaluate various types of advanced trackers. We provide extensive evaluations and detailed analyses of 43 representative trackers and envision future research directions in the field of deep UAV tracking and beyond. The dataset, toolkits, and baseline results are available at https://github.com/983632847/WebUAV-3M.

With the rapid development in the field of computer vision, the vision-based approach to unmanned aerial vehicle (UAV) tracking and landing technology in weak global positioning system (GPS) or GPS-free environments has become prominent in military and civilian missions. However, this technique still suffers from problems such as interference by similar targets in the environment, low tracking accuracy, slow processing speed, and poor stability. To solve these problems, we propose the designated target anti-interference tracking (DTAT) method, which integrates YOLOv5 and SiamRPN, and built a system to achieve UAV tracking and the landing of a designated target in an environment with multiple interference targets. The system consists of the following parts: first, an image is acquired by a monocular camera to obtain the pixel position information of the designated target. Next, the position of the UAV relative to the target is estimated based on the pixel location information of the target and the known target size information. Finally, the discrete proportion integration differentiation (PID) control law is used to complete the target tracking and landing task of the UAV. To test the system performance, we deployed it on a robot operating system (ROS) platform, conducted many simulation experiments, and observed the real-time trajectories of the UAV and the target through Gazebo software. The results show that the relative distance between the UAV and the target during the tracking process when the target was moving at 0.6 m/s does not exceed 0.8 m, and the landing error of the UAV during the landing process after the target is stationary does not exceed 0.01 m. The results validate the effectiveness and robustness of the system and lay a foundation for subsequent research.

As a significant research direction in remote sensing fields, unmanned aerial vehicles (UAVs) tracking has achieved rapid development in recent years. However, due to limited power and computation resources on aerial platforms, the tracking methods deployed on UAVs usually require high computational efficiency and performance. In addition, various challenges (i.e., similar object, background clutter and occlusion) have inevitably occurred during the UAV tracking phase. Therefore, considering the above issues comprehensively, this paper proposes a dynamic aberrance-repressed temporal regularized correlation filter to achieve stable tracking in UAV remote sensing videos. First, we have introduced the aberrance-repressed temporal regularizations into the discriminative correlation filter (DCF) framework. Second, a novel objective loss function is constructed to adjust the strength of each regularization for training the filter. Then, a new judgment mechanism based on the response variation is exploited to reflect the response fluctuation and applied to tune parameters of both regularizations. Finally, comprehensive experiments are done on three different UAV benchmarks, i.e., UAV123@10fps, UAVDT and VisDrone2018, to verify the performance of our tracker and have demonstrated that our tracker achieves superior performance against other total 25 state-of-the-art trackers while reaching <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 35 FPS on a single CPU.

Unmanned Aerial Vehicles (UAV), such as quadrotors or fixed-wing aircraft, are increasingly common in airspace between 100 and 2000 meters. Extending current aviation infrastructure management operations, such as flight plan approval or aircraft tracking, to include UAV systems is difficult for multiple reasons, such as the technical challenge of tracking objects that frequently travel below the radar floor. This work explores the feasibility of using cellular data modems to achieve low-cost UAV tracking solutions for small consumer UAVs, such as *rotors, which typically have a very low-altitude flight path. We also explore the altitude at which the data modem functionality diminishes. We present a UAV tracking architecture and web service interface, as well as the empirical evidence gathered from testing the real-time capabilities and limitations of such a system.

Siamese-based trackers have shown excellent performance in unmanned aerial vehicle (UAV) tracking due to their balanced accuracy and speed. However, most existing methods rely heavily on local convolution operations of CNNs for feature extraction, which fails to establish global feature relationships. In addition, the challenge of information imbalance, caused by differences in background and the prevalence of single-object scenarios, remains a significant obstacle in the current domain. This can potentially result in unwanted boundary effects in UAV tracking. To address these challenges, this article introduces a novel tracker called eccentric loss and adaptive search (ELAS), which is specifically designed for UAV videos and aims to overcome these challenges. The proposed ELAS enhances feature representation capabilities by utilizing multi-input and multimapping of information. It also addresses information imbalance through a differentiated loss function. Furthermore, an adaptive search strategy is introduced to accommodate varying search scales across different scenarios. Specifically, an additional multilayer feature enhancement (MLE) network is first introduced, which absorbs the middle and high layer information in the backbone network and directs the features to focus on global connections using self-attention, avoiding the loss of useful semantic information due to falling into local optima. Then, a novel eccentric loss is designed to guide network training, enhancing the saliency of object features on the feature map by accounting for the volatility of negative samples and estimating classification performance. Finally, an adaptive multiscale search strategy is proposed to detect the saliency of objects through between-class segmentation effectiveness and, thus, adjust the proportional scale parameter. Extensive experiments on challenging UAV benchmarks demonstrate that ELAS achieves state-of-the-art (SOTA) performance with real-time speed, confirming its effectiveness and robustness.

Object tracking is a fundamental task for the visual perception system on the intelligent unmanned aerial vehicle (UAV). The high efficiency of correlation filter (CF) based trackers has advanced the widespread development of online UAV object tracking. This kind of method can effectively train a filter to discriminate the target from the background. However, most CF-based methods require a fixed label function over all the previous samples, leading to over-fitting and filter degradation, especially in complex drone scenarios. To address this problem, a novel adaptive response reasoning approach is proposed for CF learning. It can leverage temporal information in filter training and significantly promote the robustness of the tracker. Specifically, the proposed response reasoning method goes beyond the standard response consistency requirement and constructs an auxiliary label of the current sample. Besides, it helps learn a generic relationship between the previous and current filters, thereby realizing self-regulated filter updating and enhancing the discriminability of the filter. Extensive experiments on four well-known challenging UAV tracking benchmarks with 278 videos sequences show that the presented method yields superior results to 40 state-of-the-art trackers with real-time performance on a single CPU, which is suitable for UAV online tracking missions.

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather. Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications. This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.

Real-time object tracking for unmanned aerial vehicles (UAVs) is an essential and challenging research topic for computer vision. However, the scenarios that UAVs deal with are complicated, and the UAV tracking targets are small. Therefore, general trackers often fail to take full advantage of their performance in UAV scenarios. In this paper, we propose a tracking method for UAV scenes, which utilizes background cues and aberrances response suppression mechanism to track in 4 degrees-of-freedom. Firstly, we consider the tracking task as a similarity measurement problem. In this study, we decompose this problem into two subproblems for optimization. Secondly, to alleviate the problem of small targets in UAV scenes, we utilize background cues fully. Also, to reduce interference by background information, we employ an aberrance response suppression mechanism. Then, to obtain accurate target state information, we introduce a logarithmic polar coordinate system. We perform phase correlation calculations in logarithmic polar coordinates to obtain the rotation and scale changes of the target. Finally, target states are obtained through response fusion, which includes displacement, scale, and rotation angle. Our approach is carried out in a large number of experiments on various UAV datasets, such as UAV123, DBT70, and UAVDT2019. Compared with the current advanced trackers, our method is superior on UAV small target tracking.

Lately unmanned aerial vehicles (UAV) became an unseparated part of modern life. These machines gained their particular popularity in the field of amateur or professional video production and small cargo deliveries. But they are not only limited by civilian lines of activity. Nowadays they are also used in military conflicts either as scout or as a deadly precise weapon. Due to UAV’s easy market access, these drones can be used by the hands of terrorists groups. It’s not a rocket science to attach an explosive device to UAV, guide it to strategic area and detonate it from far away. This is one of the main danger sources for such places like nuclear power plants. A downward trend also led to new type of thread – swarm attacks, when group of small UAVs strikes their victim. A timely detection of such attacks is not a trivial task. This question is being studied by many researches from different fields. The most promising methods comes from radiolocation and computer vision areas. Next in the paper, modern algorithms of detection, classification and tracking of UAVs by using optical flow will be reviewed. Usually, the common approach for this task comes from classic computer vision methods or, highly popular today, deep learning methods.

UAVs are divided into two major classes: military and civilian. These classes overlap conceptually, because there are core missions that are common to both the military and civilian world. However, almost all specific UAVs are intended for one or the other market because of the different environments in which they are required to operate. The civilian UAV market is further divided between some commercial and government applications that required relatively large and expensive UAVs and a rapidly growing market for small and inexpensive UAVs that have been applied to an equally rapidly growing list of applications. Classes and missions of unmanned aerial vehicles (UAVs) are discussed in terms of both various official systems of classification developed by government agencies and informal classifications by size and/or primary markets. These discussions are illustrated by representative examples.

Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently.

Nowadays, owing to the communication, computation, storage, networking, and sensing abilities, the swarm of unmanned aerial vehicles (UAV) is highly anticipated to be helpful for emergency, disaster, and military situations. Additionally, in such situations, each UAV generates local sensing data with its cameras and sensors. Data sharing in UAV swarm is an urgent need for both users and administrators. For users, they may want to access data stored on any specific UAV on demand. For administrators, they need to construct global information and situational awareness to enable many cooperative applications. This paper makes the first step to tackling this open problem with an efficient data-sharing framework called Frisbee. It first groups all UAVs as a series of cells, each of which has a head-UAV. Inside any cell, all UAVs can communicate with each other directly. Thus, for the intra-cell sharing, Frisbee designs the Dynamic Cuckoo Summary for the head-UAV to accurately index all data inside the cell. For inter-cell sharing, Frisbee designs an effective method to map both the data indices and the head-UAV into a 2-dimensional virtual plane. Based on such virtual plane, a head-UAV communication graph is formed according to the communication range of each head for both data localization and transmission. The comprehensive experiments show that Frisbee achieves 14.7% higher insert throughput, 39.1% lower response delay, and 41.4% less implementation overhead, respectively, compared to the most involved solutions of the ground network.

The main prospects for the group use of unmanned aerial vehicles (UAVs) were analyzed, the problems were identified, and the stages of its solution were proposed. The basic algorithms are analyzed, which are designed to achieve joint and coordinated actions of all elements of the system of integrated control of a group of UAVs, and are used in most approaches to ensure stable interaction between system components. The analysis of the most common mathematical models for controlling groups of UAVs was carried out, in particular: the Reynolds cohesion model, the artificial potential field model, and the leader-following model. The peculiarities of the operation of these models, common and distinctive features, advantages and disadvantages are determined. Based on the considered models and basic interaction algorithms, the algorithms of the components of the UAV group control system are proposed, and the sequence of actions during the interaction between the system components is also described. The main details of UAV group control algorithms are considered. The main aspects of the interaction of the system elements are given, which include messages of various categories used at different stages of the system's operation, in particular: synchronizing messages; group-wide control messages for managing the group as a whole; messages for correcting the flight of a specific UAV; notification of the transfer of statistical information for flight correction; notification of the launch and coordination of the task execution by a specific UAV. The use of joint messages for group management is described, which allows you to relieve the transmission channel, because the flight trajectories are calculated by each UAV individually, according to predetermined criteria. The main stages of the system's operation, the peculiarities of their implementation, the sequence of message transmission in each stage, as well as the basic details of the operation of some system components are described. Block diagrams are provided to display the process of system operation at various stages of flight, with a detailed description of the steps of system operation. The process of collecting statistical data from UAVs for dynamic flight adjustment of the group is described.